license: other base_model:

• abenzerps/Qwen-Image-2.1-Uncensored-Quantized tags:
• gguf
• text-to-image
• qwen-image
• quantization
• mixed-precision
• stable-diffusion.cpp
• comfyui library_name: gguf pipeline_tag: text-to-image language:
• en

Qwen-Image-2.1 Uncensored — MIXED 4.39 GB (GGUF)

A per-layer mixed-precision quantisation of abenzerps/Qwen-Image-2.1-Uncensored-Quantized, built from its BF16 GGUF (14.23 GB).

4.391 GB · 4.94 bits/weight · relative weight error 0.0636

Every one of the 297 tensors was quantised independently, with all other tensors held at full precision, and the format was then chosen per tensor to minimise total reconstruction error under a size budget. Result: Q4_K quality budget, ~10 % better fidelity than uniform Q4_K, at 4.39 GB instead of 4.00 GB.

preview

Generated with this file: 1024×1024, 20 steps, euler, CFG 6.0, seed 42. No reference image.

Files

Composition of the mixed build: Q4_K ×126, Q5_K ×105, F16 ×66. The 66 F16 tensors are the block RMS norms plus two odd-shaped tensors (ne0 = 64), which cannot be block-quantised; they are ~0.01 % of the file.

How the bits were assigned

For each tensor, five encodings were built and the true error ||W − W_q||_F² measured. Formats were then allocated globally to minimise

sum_tensors ||W − W_q||_F^2     subject to     total size <= budget

which is the expected output-perturbation energy under an isotropic-input assumption. The allocation is a Lagrangian λ-sweep over the measured per-tensor byte/error pairs.

per-block precision

Sensitivity is nearly flat across depth (block-level relative error 0.043–0.084), so most of the gain comes from choosing better formats for a subset of tensors rather than from redistributing bits. Early blocks and the most sensitive late attn.to_v / modulation.1 tensors receive Q5_K; the rest run Q4_K.

size vs fidelity

Quality

Measured by actually rendering, not only in weight space.

Engine stable-diffusion.cpp (master-975-95b3090, CUDA 12), RTX 4070 Laptop 8 GB with --offload-to-cpu, 1024×1024, 20 steps, euler, CFG 6.0.

The mixed build wins on 3 of 4 subjects. At 1:1 crop, skin pores, hair and fabric weave are indistinguishable between BF16 and both quantised builds.

detail comparison

Differences between the two quantised builds at image level are mostly the sampler amplifying sub-percent latent drift, not structural damage; the weight-space numbers above are the reliable comparison.

Usage

stable-diffusion.cpp

sd-cli.exe \
  --diffusion-model qwen-image-2.1-UC-MIXED-4.39G.gguf \
  --vae qwen_image_2.1_vae_bf16.safetensors \
  --llm Qwen3VL-8B-Instruct-Q4_K_M.gguf \
  -p "a weathered stone lighthouse on a basalt cliff during a storm at blue hour" \
  --cfg-scale 6.0 --sampling-method euler --steps 20 \
  -W 1024 -H 1024 --seed 42 --offload-to-cpu -o out.png

Needs the Qwen-Image-2.1 VAE and a Qwen3-VL-8B text encoder; both are available from Comfy-Org/Qwen-Image-2.1.

ComfyUI

Drop into models/diffusion_models/, load with Unet Loader (GGUF) from ComfyUI-GGUF, and set the CLIPLoader type to qwen_image.

Notes and caveats

• Use 1024 or larger. At 512×512 this model produces noise — including from the pristine BF16 weights. It is a 1024-class model.
• Dimensions should be divisible by 32.
• The error metric assumes isotropic inputs; activation-aware allocation would need calibration data and was not used.
• Q4_K is the lowest format implemented here, so 4.00 GB is the floor for uniform builds. Q3_K/Q2_K are not included.
• Generated samples inherit the uncensored behaviour and biases of the base model. You are responsible for how you use it.

Credits

• Base: abenzerps/Qwen-Image-2.1-Uncensored-Quantized
• Upstream: Qwen/Qwen-Image-2.1
• Engine: leejet/stable-diffusion.cpp

Quantisation, measurements and samples produced locally. Licence follows the base model (other); check its terms before redistribution or commercial use.